323 research outputs found
FORECAST OF THE EXPECTED NON-EPIDEMIC MORBIDITY OF ACUTE DISEASES USING RESAMPLING METHODS
In epidemiological surveillance it is important that any unusual increase of reported cases be detected as rapidly as possible. Reliable forecasting based on a suitable time series model for an epidemiological indicator is necessary for estimating the expected non-epidemic indicator and to elaborate an alert threshold. Time series analysis of acute diseases often use Gaussian autoregressive integrated moving average models. However, these approaches could be adversely affected by departures from the true underlying distribution. The objective of this paper is to introduce a bootstrap procedure for obtaining prediction intervals in linear models in order to avoid the normality assumption. We present a Monte Carlo study comparing the finite sample properties of the bootstrap prediction intervals with those of alternative methods. Finally, we illustrate the performance of the proposed method with a meningococcal disease incidence series.
INTRODUCING MODEL UNCERTAINTY IN TIME SERIES BOOTSTRAP
It is common in parametric bootstrap to select the model from the data, and then treat it as it were the true model. Kilian (1998) have shown that ignoring the model uncertainty may seriously undermine the coverage accuracy of bootstrap confidence intervals for impulse response estimates which are closely related with multi-step-ahead prediction intervals. In this paper, we propose different ways of introducing the model selection step in the resampling algorithm. We present a Monte Carlo study comparing the finite sample properties of the proposed method with those of alternative methods in the case of prediction intervals.
Classification of functional data: a weighted distance approach
A popular approach for classifying functional data is based on the distances from the function or its derivatives to group representative (usually the mean) functions or their derivatives. In this paper, we propose using a combination of those
distances. Simulation studies show that our procedure performs very well, resulting
in smaller testing classication errors. Applications to real data show that our
procedure performs as well as –and in some cases better than– other classication
methods
Forecasting time series with sieve bootstrap
In this paper we consider bootstrap methods for constructing nonparametric prediction intervals for a general class of linear processes. Our approach uses the sieve bootstrap procedure of Biihlmann (1997) based on residual resampling from an autoregressive approximation to the given process. We show that the sieve bootstrap provides consistent estimators of the conditional distribution of future values given the observed data, assuming that the order of the autoregressive approximation increases with the sample size at a suitable rate and some restrictions about polynomial decay of the coefficients ~ j t:o of the process MA(oo) representation. We present a Monte Carlo study comparing the finite sample properties of the sieve bootstrap with those of alternative methods. Finally, we illustrate the performance of the proposed method with real data examples
ILLANES, José Luis (coord.) – Diccionario de San Josemaría Escrivá de Balaguer. Burgos: Editorial Monte Carmelo; Instituto Histórico San Josemaría Escrivá de Balaguer, 2013. 1360 p. ISBN 978-84-8353-592-9
MAYO ESCUDERO, Juan - As cartuxas de Portugal através dos séculos: crónicas das cartuxas portuguesas. Salzburg: Institut für Anglistik und Amerikanistik, Universität Salzburg, 2011. 364 p. (Analecta Cartusiana, 268)
CARDIM, Pedro; COSTA, Leonor Freire; CUNHA, Mafalda Soares da, org. – Portugal na Monarquia Hispânica: dinâmicas de integração e conflito. Vas Jornadas Internacionais da Red Columnária – História das monarquias ibéricas
FRANCO, José Eduardo; ABREU, Luís Machado de (coord.) – Para a História das Ordens e Congregações Religiosas em Portugal, na Europa e no Mundo. Prior Velho: Paulinas, 2014. 2 vols. (944+1040 p.) + CD. ISBN 978-989-673-334-6
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